mlsanityData to 7 Oct 2026

A Novel Feature Representation for Malware Classification

arXiv 2210.09580 ↗First posted 18 Oct 2022cs.CR cs.LGcited by 1

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Abstract (arXiv)

In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a bottom-up approach, and analyzed the overlap of malicious and benign programs in terms of their components. We have shown that our method of analysis offers an increase in feature resolution that is descriptive of data movement in comparison to tf-idf features.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 44 references that are in the map. First posted on arXiv 18 Oct 2022.

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3 of 44 references.

References · 3

  1. 2019Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networkscited by 266
  2. 2020Learning Semantic Program Embeddings with Graph Interval Neural Networkcited by 20
  3. 2022Empirical Network Structure of Malicious Programscited by 2

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  1. 2024kNN Classification of Malware Data Dependency Graph Featurescited by 0